Recent growth in the social network, peer-to-peer (P2P), and streaming media markets has been profound. They are indeed ubiquitous and are becoming mainstream parts of everyday society and most importantly, they are places where people go to get information and entertainment as a primary source. Customers rely on Amazon recommendations to decide what book to read next. They rely on Netflix recommendations to decide what movies to watch. Music lovers rarely listen to the radio because all they could ever want can be streamed to whatever device they happen to be using.
However, as ubiquitous as social networks and streaming media may be, they are only loosely connected. Services that maintain their own integrated social network (e.g. Amazon, Netflix) benefit from their social network alone but not others. Some of these have powerful analytical processing on their closed network of customers/users but their capabilities are severely limited by the closed nature of their proprietary network. Furthermore, for P2P networks, there are very few centralized servers for hosting, analyzing, and delivering metadata about media to users. By its very nature, everything is distributed.
There is far more information floating around the Internet about visual and audio media than could possibly be captured in a single closed network. Even the largest and most successful social networks are not linked to any specific media (e.g. Facebook,). While Facebook, for example, allows users to embed YouTube and other videos into postings, to our knowledge, neither the social network (e.g. Facebook) nor the media source (e.g. YouTube) makes use of that information. Most importantly, a very common topic of discussion on these social networks is media—TV shows that people like, movies they have seen, music they like and want to tell friends about. This is a rich source of untapped information that could be harnessed to assist users in finding media they want and would enjoy, and that could inform media developers and distributors to better know what is wanted, where, and when. The potential for highly granular demand signals is hiding in this data.
What is needed is a system that mines the data associated with P2P networks for activity and semantic associations and then links that to related activity on social networks. The present invention solves this problem by connecting P2P streaming media networks (e.g. BitTorrent) with social media and data analytics. The resulting system provides users with finely granular recommendations and content demand insights based on a combination of key features (such as social sentiment gleaned from the social networks) as well as entertainment domain specific features extracted from P2P networks. It then identifies and sources new media streams (Torrents) based on those recommendations and demand insights. The system further provides highly detailed sentiment information to content developers and distributors for the purposes of tailoring new media production and the distribution of existing media.